Generally, a diagnostic framework provides a way for identifying the faulty components in a device and suggesting the remedies for the faulty components. Different diagnostic frameworks are present in the market today. Typically, the diagnostic frameworks that are available for detecting the symptoms depend on knowledge base and simple mathematical models. As is widely known, the existing diagnostic frameworks predict the faulty components of a device using Bayesian Networks (BN). Further, these frameworks generally predict the repair strategy for the faulty components of a device based on cost estimation before and after carrying out repairable procedures, expected cost of repair and expected cost of repair after testing values.
The conventional approaches for detecting symptoms and determining an optimal remedy pattern for a faulty device fail in modeling noise in the sensor data accurately and hence, lack relationship between the symptoms and faultiness of the components. This noise, which may be due to environmental factors or unreliability in the components, can infect the true readings from the device, and can corrupt the data to such an extent that it might indicate a fault erroneously. Further, the noise can also mask true faults. Additionally, the existing framework is not generic for different types of devices such as medical devices, communication devices, electronic devices and the like.
Therefore, there is a need for a robust statistical model that can determine sensor data from sensors indicating ‘true faults’ in the device and suppress the false indications due to noise, thereby establishing a true relationship between the symptoms and faultiness of the components. Further, there is a need for a framework that is generic for different type of devices.